Spending patterns are a predictor of financial personality [1]}. In their paper, the authors trained a random forest to predict customer personalities from their classified financial transactions, using a prevalent taxonomy of personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Although they achieved only a modest predictive accuracy, a subsequent study found that spending patterns over time expose salient information that is obscured in non-temporal form [2]}; the authors in this study used the same personality model, but added temporal patterns such as variability of the amount, persistence of the category in time, and burstiness—the intermittent changes in frequency of an event. Recurrent neural networks (RNNs) are able to extract this salient information when predicting personality traits from financial transactions [3]}. In, [4]}, we gained an understanding of these extracted features by interpreting the dynamics of the RNN state space through a set of attractors. Understanding model behavior is crucial in industries such as personal finance [5]}. In their study, [5]} extracted rules from three classes of models—linear regression, logistic regression, and random forests—which not only exposed the spending patterns most indicative of personality traits, but also aided in model improvement.
